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creslinux 2018-07-16 14:16:35 +00:00
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commit fb0edd71ff
1 changed files with 171 additions and 105 deletions
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freqtrade/optimize/backtesting.py
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@ -73,6 +73,35 @@ class Backtesting(object):
self.stop_loss_value = self.analyze.strategy.stoploss
#### backslap config
'''
Numpy arrays are used for 100x speed up
We requires setting Int values for
buy stop triggers and stop calculated on
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
'''
self.np_buy: int = 0
self.np_open: int = 1
self.np_close: int = 2
self.np_sell: int = 3
self.np_high: int = 4
self.np_low: int = 5
self.np_stop: int = 6
self.np_bto: int = self.np_close # buys_triggered_on - should be close
self.np_bco: int = self.np_open # buys calculated on - open of the next candle.
# self.np_sto: int = self.np_low # stops_triggered_on - Should be low, FT uses close
# self.np_sco: int = self.np_stop # stops_calculated_on - Should be stop, FT uses close
self.np_sto: int = self.np_close # stops_triggered_on - Should be low, FT uses close
self.np_sco: int = self.np_close # stops_calculated_on - Should be stop, FT uses close
self.use_backslap = True # Enable backslap
self.debug = False # Main debug enable, very print heavy, enable 2 loops recommended
self.debug_timing = False # Stages within Backslap
self.debug_2loops = False # Limit each pair to two loops, useful when debugging
self.debug_vector = False # Debug vector calcs
self.debug_timing_main_loop = False # print overall timing per pair
@staticmethod
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
@ -215,7 +244,7 @@ class Backtesting(object):
realistic: do we try to simulate realistic trades? (default: True)
:return: DataFrame
"""
debug_timing = False
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
processed = args['processed']
@ -224,99 +253,114 @@ class Backtesting(object):
trades = []
trade_count_lock: Dict = {}
########################### Call out BSlap instead of using FT
bslap_results: list = []
use_backslap = self.use_backslap
debug_timing = self.debug_timing_main_loop
if use_backslap: # Use Back Slap code
########################### Call out BSlap Loop instead of Original BT code
bslap_results: list = []
for pair, pair_data in processed.items():
if debug_timing: # Start timer
fl = self.s()
ticker_data = self.populate_sell_trend(
self.populate_buy_trend(pair_data))[headers].copy()
if debug_timing: # print time taken
flt = self.f(fl)
#print("populate_buy_trend:", pair, round(flt, 10))
st = self.s()
# #dump same DFs to disk for offline testing in scratch
# f_pair:str = pair
# csv = f_pair.replace("/", "_")
# csv="/Users/creslin/PycharmProjects/freqtrade_new/frames/" + csv
# ticker_data.to_csv(csv, sep='\t', encoding='utf-8')
#call bslap - results are a list of dicts
bslap_pair_results = self.backslap_pair(ticker_data, pair)
last_bslap_results = bslap_results
bslap_results = last_bslap_results + bslap_pair_results
if debug_timing: # print time taken
tt = self.f(st)
print("Time to BackSlap :", pair, round(tt,10))
print("-----------------------")
for pair, pair_data in processed.items():
if debug_timing: # Start timer
fl = self.s()
# Switch List of Trade Dicts (bslap_results) to Dataframe
# Fill missing, calculable columns, profit, duration , abs etc.
bslap_results_df = DataFrame(bslap_results)
bslap_results_df['open_time'] = to_datetime(bslap_results_df['open_time'])
bslap_results_df['close_time'] = to_datetime(bslap_results_df['close_time'])
ticker_data = self.populate_sell_trend(
### don't use this, itll drop exit type field
# bslap_results_df = DataFrame(bslap_results, columns=BacktestResult._fields)
bslap_results_df = self.vector_fill_results_table(bslap_results_df)
return bslap_results_df
else: # use Original Back test code
########################## Original BT loop
for pair, pair_data in processed.items():
if debug_timing: # Start timer
fl = self.s()
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
ticker_data = self.populate_sell_trend(
self.populate_buy_trend(pair_data))[headers].copy()
ticker_data.drop(ticker_data.head(1).index, inplace=True)
if debug_timing: # print time taken
flt = self.f(fl)
print("populate_buy_trend:", pair, round(flt, 10))
st = self.st()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
# #dump same DFs to disk for offline testing in scratch
# f_pair:str = pair
# csv = f_pair.replace("/", "_")
# csv="/Users/creslin/PycharmProjects/freqtrade_new/frames/" + csv
# ticker_data.to_csv(csv, sep='\t', encoding='utf-8')
ticker_data.drop(ticker_data.head(1).index, inplace=True)
#call bslap - results are a list of dicts
bslap_pair_results = self.backslap_pair(ticker_data, pair)
last_bslap_results = bslap_results
bslap_results = last_bslap_results + bslap_pair_results
if debug_timing: # print time taken
flt = self.f(fl)
#print("populate_buy_trend:", pair, round(flt, 10))
st = self.s()
if debug_timing: # print time taken
tt = self.f(st)
print("Time to Back Slap :", pair, round(tt,10))
print("-----------------------")
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker = [x for x in ticker_data.itertuples()]
lock_pair_until = None
for index, row in enumerate(ticker):
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if realistic:
if lock_pair_until is not None and row.date <= lock_pair_until:
continue
if max_open_trades > 0:
# Check if max_open_trades has already been reached for the given date
if not trade_count_lock.get(row.date, 0) < max_open_trades:
continue
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
trade_count_lock, args)
# Switch List of Trade Dicts (bslap_results) to Dataframe
# Fill missing, calculable columns, profit, duration , abs etc.
bslap_results_df = DataFrame(bslap_results)
bslap_results_df['open_time'] = to_datetime(bslap_results_df['open_time'])
bslap_results_df['close_time'] = to_datetime(bslap_results_df['close_time'])
if trade_entry:
lock_pair_until = trade_entry.close_time
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
# This happens only if the buy-signal was with the last candle
lock_pair_until = ticker_data.iloc[-1].date
### don't use this, itll drop exit type field
# bslap_results_df = DataFrame(bslap_results, columns=BacktestResult._fields)
if debug_timing: # print time taken
tt = self.f(st)
print("Time to BackTest :", pair, round(tt, 10))
print("-----------------------")
bslap_results_df = self.vector_fill_results_table(bslap_results_df)
return bslap_results_df
########################### Original BT loop
# for pair, pair_data in processed.items():
# pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
#
# ticker_data = self.populate_sell_trend(
# self.populate_buy_trend(pair_data))[headers].copy()
#
# # to avoid using data from future, we buy/sell with signal from previous candle
# ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
# ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
#
# ticker_data.drop(ticker_data.head(1).index, inplace=True)
#
# # Convert from Pandas to list for performance reasons
# # (Looping Pandas is slow.)
# ticker = [x for x in ticker_data.itertuples()]
#
# lock_pair_until = None
# for index, row in enumerate(ticker):
# if row.buy == 0 or row.sell == 1:
# continue # skip rows where no buy signal or that would immediately sell off
#
# if realistic:
# if lock_pair_until is not None and row.date <= lock_pair_until:
# continue
# if max_open_trades > 0:
# # Check if max_open_trades has already been reached for the given date
# if not trade_count_lock.get(row.date, 0) < max_open_trades:
# continue
#
# trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
#
# trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
# trade_count_lock, args)
#
#
# if trade_entry:
# lock_pair_until = trade_entry.close_time
# trades.append(trade_entry)
# else:
# # Set lock_pair_until to end of testing period if trade could not be closed
# # This happens only if the buy-signal was with the last candle
# lock_pair_until = ticker_data.iloc[-1].date
#
# return DataFrame.from_records(trades, columns=BacktestResult._fields)
######################## Original BT loop end
return DataFrame.from_records(trades, columns=BacktestResult._fields)
####################### Original BT loop end
def vector_fill_results_table(self, bslap_results_df: DataFrame):
"""
@ -333,13 +377,18 @@ class Backtesting(object):
:return: bslap_results Dataframe
"""
import pandas as pd
debug = False
debug = self.debug_vector
# stake and fees
# stake = 0.015
# 0.05% is 0.0005
#fee = 0.001
stake = self.config.get('stake_amount')
fee = self.fee
open_fee = fee / 2
close_fee = fee / 2
# stake and fees
stake = 0.015
# 0.05% is 0.00,05
open_fee = 0.0000
close_fee = 0.0000
if debug:
print("Stake is,", stake, "the sum of currency to spend per trade")
print("The open fee is", open_fee, "The close fee is", close_fee)
@ -420,9 +469,12 @@ class Backtesting(object):
from datetime import datetime
### backslap debug wrap
debug_2loops = False # only loop twice, for faster debug
debug_timing = False # print timing for each step
debug = False # print values, to check accuracy
# debug_2loops = False # only loop twice, for faster debug
# debug_timing = False # print timing for each step
# debug = False # print values, to check accuracy
debug_2loops = self.debug_2loops # only loop twice, for faster debug
debug_timing = self.debug_timing # print timing for each step
debug = self.debug # print values, to check accuracy
# Read Stop Loss Values and Stake
stop = self.stop_loss_value
@ -451,20 +503,34 @@ class Backtesting(object):
buy stop triggers and stop calculated on
# buy 0 - open 1 - close 2 - sell 3 - high 4 - low 5 - stop 6
'''
np_buy: int = 0
np_open: int = 1
np_close: int = 2
np_sell: int = 3
np_high: int = 4
np_low: int = 5
np_stop: int = 6
np_bto: int = np_close # buys_triggered_on - should be close
np_bco: int = np_open # buys calculated on - open of the next candle.
#np_sto: int = np_low # stops_triggered_on - Should be low, FT uses close
#np_sco: int = np_stop # stops_calculated_on - Should be stop, FT uses close
np_sto: int = np_close # stops_triggered_on - Should be low, FT uses close
np_sco: int = np_close # stops_calculated_on - Should be stop, FT uses close
#
# np_buy: int = 0
# np_open: int = 1
# np_close: int = 2
# np_sell: int = 3
# np_high: int = 4
# np_low: int = 5
# np_stop: int = 6
# np_bto: int = np_close # buys_triggered_on - should be close
# np_bco: int = np_open # buys calculated on - open of the next candle.
# #np_sto: int = np_low # stops_triggered_on - Should be low, FT uses close
# #np_sco: int = np_stop # stops_calculated_on - Should be stop, FT uses close
# np_sto: int = np_close # stops_triggered_on - Should be low, FT uses close
# np_sco: int = np_close # stops_calculated_on - Should be stop, FT uses close
#######
# Use vars set at top of backtest
np_buy: int = self.np_buy
np_open: int = self.np_open
np_close: int = self.np_close
np_sell: int = self.np_sell
np_high: int = self.np_high
np_low: int = self.np_low
np_stop: int = self.np_stop
np_bto: int = self.np_bto # buys_triggered_on - should be close
np_bco: int = self.np_bco # buys calculated on - open of the next candle.
np_sto: int = self.np_sto # stops_triggered_on - Should be low, FT uses close
np_sco: int = self.np_sco # stops_calculated_on - Should be stop, FT uses close
### End Config
pair: str = pair